System and method for estimating long term characteristics of battery

ABSTRACT

A system includes a learning data input unit for receiving initial and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for long term characteristic estimation; an artificial neural network operation unit for converting the learning data into first and second data structures, allowing an artificial neural network to learn the learning data based on each data structure, converting the measurement data into first and second data structures, and individually applying the learned artificial neural network corresponding to each data structure to calculate and output long term characteristic estimation data based on each data structure; and a long term characteristic evaluation unit for calculating an error of the estimation data of each data structure and determining reliability of the estimation data depending on error.

TECHNICAL FIELD

The present invention relates to system and method for estimating longterm characteristics of a battery, and more particularly to system andmethod for estimating long term characteristics of a battery based oninitial characteristics of the battery.

BACKGROUND ART

A battery exhibits decreased capacity and performance as its usage isincreased. Thus, it is very important to design a battery such that itslong term characteristics are maintained though it is used for a longtime. The long term characteristics may be a charging capacity, adischarging capacity, a thickness and an open circuit voltage of abattery, as examples.

The long term characteristics of a battery are measured based on a cyclecharging/discharging process. In the cycle charging/discharging process,a certain number of batteries are sampled from a produced battery lot,and then the sampled batteries are successively charged/discharged. Iflong term characteristics of the sampled batteries satisfy certaincriteria at predetermined long term cycles, the corresponding batterylot is considered as being successful. For example, the correspondingbattery lot is considered as being successful when a dischargingcapacity at 3V is 75% or above of an initial capacity at 300 cycle (300time charging/discharging).

However, a long time is consumed for estimating long termcharacteristics of a battery. For example, a charging/dischargingprocess of 300 cycles takes a long time of about 3 months. Thus, inorder to measure long term characteristics of a battery based on thecycle charging/discharging process, shipment of batteries is delayedduring the time for the charging/discharging process, thereby increasingburdens in stockpile.

Thus, in a conventional case, if one lot of batteries is produced, acertain number of batteries are sampled and then the batteries areshipped instantly, and then long term characteristics of the sampledbatteries are evaluated to take suitable measures afterward, which iscalled ‘post-shipment evaluation’. If any problem is discovered as aresult of evaluation of long term characteristics after batteries areshipped, the battery lot corresponding to the sampled batteries isdetermined as having bad long term characteristics. After that, theshipped batteries are called back, and a countermeasure for removing afactor of such inferiority of long term characteristics is studied andthen taken to a battery production process. However, such apost-shipment evaluation method shows the following problems.

First, in case any specific battery lot is determined as being bad, aneconomic cost (e.g., a distribution cost) is consumed in retrievingbatteries of the corresponding lot.

Second, in case a battery having bad long term characteristics is soldto an end user, it is substantially impossible to take a measure to thebattery, for example to retrieve the battery.

Third, in case it is determined that a defect exists in a productionprocess as a result of analysis of inferior long characteristics, longterm characteristics of all batteries produced by the same productionprocess become in question, so loss of a manufacturer is increased asmuch.

Fourth, if a battery with bad long term characteristics is sold and usedin an electronic product, a feeling of satisfaction for the battery isdeteriorated, so reliability of a battery manufacturer and a seller isalso deteriorated.

Thus, there is an urgent need to a scheme capable of reliably estimatinglong term characteristics of batteries in the related art before thebatteries are shipped.

DISCLOSURE OF INVENTION Technical Problem

The present invention is designed to solve the problems of the priorart, and therefore it is an object of the present invention to providesystem and method for estimating long term characteristics of a battery,which enables fast inferiority determination by estimating long termcharacteristics of a battery based on its initial characteristics, andfurther enables total inspection of the battery by using characteristicsmeasured by all batteries, for example a charge data at an activationprocess.

Technical Solution

In order to accomplish the above object, the present invention providesa system for estimating long term characteristics of a battery,comprising: a learning data input unit for receiving initialcharacteristic learning data and long term characteristic learning dataof a battery to be a learning object; a measurement data input unit forreceiving initial characteristic measurement data of a battery to be anobject for estimation of long term characteristics; an artificial neuralnetwork operation unit for converting the initial characteristiclearning data and the long term characteristic learning data into firstand second data structures, allowing an artificial neural network tolearn the initial characteristic learning data and the long termcharacteristic learning data based on each data structure, convertingthe input initial characteristic measurement data into first and seconddata structures, and individually applying the learned artificial neuralnetwork corresponding to each data structure to calculate and outputlong term characteristic estimation data based on each data structure;and a long term characteristic evaluation unit for calculating an errorof the output long term characteristic estimation data of each datastructure and determining reliability of the long term characteristicestimation data depending on the error.

Preferably, the learned artificial neural network based on each datastructure has at least one neuron layer arranged in series. The neuronlayer converts an input vector into an output vector. At this time, abias vector and a weight matrix calculated by the learning of theartificial neural network are reflected on the input vector, the inputvector on which the bias vector and the weight matrix are reflected isprocessed by a neuron transfer function, and then result of the neurontransfer function is output as an output vector. In the serialarrangement of the neuron layer, the first neuron layer has an inputvector composed of initial characteristic measurement data. Also, anoutput vector of the last neuron layer is a long term characteristicestimation vector.

Preferably, wherein the data relating to initial characteristicsincludes a charging characteristic variation data of a battery, measuredin a battery activating process; or a charging characteristic variationdata, a discharging characteristic variation data, a thickness variationdata, or an open circuit voltage variation data of a battery, obtainedby measurement of initial cycle characteristics. Also, the data relatingto long term characteristics includes a charging characteristicvariation data, a discharging characteristic variation data, a thicknessvariation data, or an open circuit voltage variation data of a batteryat predetermined long term cycles.

The system according to the present invention may further include aninitial characteristic measurement sensor for measuring a chargingcharacteristic of a battery put into an activating process and thenoutputting the measured charging characteristic as an initialcharacteristic measurement data, and the measurement data input unit mayreceive the initial characteristic measurement data from the initialcharacteristic measurement sensor.

The system according to the present invention may further include adisplay for receiving the long term characteristic estimation datacalculated based on each data structure from the artificial neuralnetwork operation unit to display the long term characteristicestimation data in a graphic-user interface through a display device.

In the present invention, in case the error is less than a criterionvalue, the long term characteristic evaluation unit determines any oneof the long term characteristic estimation data calculated based on eachdata structure or average data of the long term characteristicestimation data as a long term characteristic estimation data, and thenoutputs the long term characteristic estimation data.

Preferably, the long term characteristic evaluation unit determines longterm characteristic quality of the battery by comparing the determinedlong term characteristic estimation data with a criterion long termcharacteristic data, and the long term characteristic evaluation unitoutputs a long term characteristic quality determination result of thebattery in a graphic-user interface through a display device.

In another aspect of the present invention, there is also provided amethod for estimating long term characteristics of a battery,comprising: receiving initial characteristic learning data and long termcharacteristic learning data of a battery to be a learning object;converting the received initial characteristic learning data and thereceived long term characteristic learning data into first and seconddata structures and then individually allowing an artificial neuralnetwork to learn based on each data structure; receiving initialcharacteristic measurement data of a battery to be an object forestimation of long term characteristics; converting the received initialcharacteristic measurement data into first and second data structures,then applying the learned artificial neural network corresponding toeach data structure thereto, and then calculating and outputting longterm characteristic estimation data based on each data structure; andcalculating an error of the output long term characteristic estimationdata based on each data structure and then determining reliability ofthe long term characteristic estimation data depending on the error.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and aspects of the present invention will become apparentfrom the following description of embodiments with reference to theaccompanying drawing in which:

FIG. 1 is a block diagram showing a system for estimating long termcharacteristics of a battery according to a first embodiment of thepresent invention;

FIG. 2 is a block diagram showing an artificial neural network structurehaving a learning ability by an artificial neural network operation unitaccording to an embodiment of the present invention;

FIG. 3 is a schematic view showing a case that initial characteristiclearning data and long term characteristic learning data are defined ina first data structure;

FIG. 4 is a schematic view showing a case that initial characteristiclearning data and long term characteristic learning data are defined ina second data structure;

FIG. 5 is a block diagram showing a system for estimating long termcharacteristics of a battery according to a second embodiment of thepresent invention;

FIG. 6 is a schematic flowchart illustrating an operation sequence ofthe system for estimating long term characteristic of a batteryaccording to the first embodiment of the present invention;

FIG. 7 is a schematic flowchart illustrating an operation sequence ofthe system for estimating long term characteristic of a batteryaccording to the second embodiment of the present invention; and

FIG. 8 is a block diagram showing an inner configuration of ageneral-purpose computer adoptable in executing an operation method forthe system for estimating long term characteristic of a batteryaccording to the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, preferred embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings. Priorto the description, it should be understood that the terms used in thespecification and the appended claims should not be construed as limitedto general and dictionary meanings, but interpreted based on themeanings and concepts corresponding to technical aspects of the presentinvention on the basis of the principle that the inventor is allowed todefine terms appropriately for the best explanation. Therefore, thedescription proposed herein is just a preferable example for the purposeof illustrations only, not intended to limit the scope of the invention,so it should be understood that other equivalents and modificationscould be made thereto without departing from the spirit and scope of theinvention.

FIG. 1 is a block diagram showing a system for estimating long termcharacteristics of a battery according to a first embodiment of thepresent invention.

Referring to FIG. 1, the system for estimating long term characteristicsof a battery according to the present invention is implemented by ageneral-purpose computer, which includes a learning data input unit 10for receiving initial characteristic learning data P_(t) and long termcharacteristic learning data T_(t) of a battery to be a learning object;a measurement data input unit 20 for receiving initial characteristicmeasurement data P_(m) of a battery to be an object for estimation oflong term characteristics; and an artificial neural network operationunit 30 for receiving the initial characteristic learning data P_(t) andthe long term characteristic learning data T_(t) from the learning datainput unit 10 to allow an artificial neural network to learn acorrelation of both learning data, receiving the initial characteristicmeasurement data P_(m) from the measurement data input unit 20 andapplying the learned artificial neural network thereto, and thuscalculating long term characteristic estimation data T_(e) from theinitial characteristic measurement data of the battery and outputtingthe long term characteristic estimation data T_(e).

The learning data input unit 10 and the measurement data input unit 20are interfaces to receive various data required for learning of theartificial neural network and calculation of long term characteristicestimation data.

The learning data input unit 10 gives a user interface having astandardized template that designates a medium file on a computerrecording initial characteristic learning data and long termcharacteristic learning data according to a predetermined protocol orallows a user to directly register initial characteristic learning dataand long term characteristic learning data, so the learning data inputunit 10 may receive initial characteristic learning data and long termcharacteristic learning data.

In addition, the measurement data input unit 20 gives a user interfacehaving a standardized template that designates a medium file on acomputer recording initial characteristic measurement data according toa predetermined protocol or allows a user to directly register initialcharacteristic measurement data, similarly to the learning data inputunit 10, so the measurement data input unit 20 may receive initialcharacteristic measurement data.

The initial characteristic learning data and the long termcharacteristic learning data are obtained through a cyclecharging/discharging process of a plurality of batteries designated aslearning objects. The cycle charging/discharging process means repeatinga process of periodically charging and discharging a battery to acertain cycle. One cycle means one charging and one discharging.

The initial characteristic learning data is a characteristic data of abattery to be a learning object, obtained at a cycle conducted in anopening part of the cycle charging/discharging process. Here, the numberof cycles at which the initial characteristic learning data is obtainedmay be changed as desired. For example, the initial characteristiclearning data may be a charging characteristic variation data of abattery, a discharging characteristic variation data, a thicknessvariation data or an open circuit voltage variation data, obtainedduring 1 to 10 cycles. Here, the charging characteristic is a chargingcurrent, a charging voltage or a charging capacity of a battery, and thedischarging characteristic is a discharging current, a dischargingvoltage or a discharging capacity of a battery. However, the presentinvention is not limited thereto. Thus, it should be understood that anyparameter capable of defining a charging characteristic or a dischargingcharacteristic of a battery can be included in the scope of parameterdefining the charging/discharging characteristic.

The initial characteristic learning data is a data relating to batterycharging characteristic, battery discharging characteristic, batterythickness or battery open circuit voltage, so it is configured as anaggregation of at least two data. For example, if the initialcharacteristic learning data is configured using a charging capacityvariation data of each charging voltage or each charging time for abattery, obtained through a charging/discharging process of 1 to 10cycles, the initial characteristic learning data includes 10 sets ofcharging capacity variation data, and the charging capacity variationdata of each set includes a plurality of charging capacity valuescorresponding to a plurality of measurement voltages and measurementtimes. Here, the measurement voltage and the measurement time formeasurement of charging capacity are determined in advance.

The long term characteristic learning data is a charging characteristicvariation data, a discharging characteristic variation data, a thicknessvariation data or an open circuit voltage variation data of a battery,obtained at a cycle conducted in a latter part of the cyclecharging/discharging process. Here, the charging characteristic is acharging current, a charging voltage or a charging capacity of abattery, and the discharging characteristic is a discharging current, adischarging voltage or a discharging capacity of a battery. However, thepresent invention is not limited thereto. Thus, it should be understoodthat any parameter capable of defining a charging characteristic or adischarging characteristic of a battery can be included in the scope ofparameter defining the charging/discharging characteristic. The numberof cycles conducted in the latter part is determined depending on thelong term characteristic specification of a battery, demanded byclients, and it may be 300 as an example. However, the present inventionis not limited to a specific number of cycles at which the long termcharacteristic learning data is obtained.

The long term characteristic learning data is a data relating to batterycharging characteristic, battery discharging characteristic, batterythickness or battery open circuit voltage, so it is configured as anaggregation of at least two data, similarly to the initialcharacteristic learning data. For example, if the long termcharacteristic learning data is configured using a charging capacityvariation data of each charging voltage or each charging time for abattery, obtained through a charging/discharging process of 300 cycles,the long term characteristic learning data includes a plurality ofcharging capacity values corresponding to a plurality of chargingvoltages and charging times, obtained during the battery chargingprocess of 300 cycles. Here, the charging voltage and the charging timefor measurement of charging capacity are determined in advance, and theyare identical to the charging voltage or the measurement time at whichthe initial characteristic learning data is obtained.

Meanwhile, the parameter relating to initial characteristics and longterm characteristics of a battery are not limited to the above in thepresent invention, and it is apparent to those having ordinary skill inthe art that any characteristic recognizable as a characteristic of abattery should be interpreted as being included in the scope of theinitial characteristic learning data and the long term characteristiclearning data.

The initial characteristic measurement data is an initial characteristicdata directly measured from a battery whose long term characteristicwill be evaluated using a cycle charging/discharging process, andattribute and kind of the data are substantially identical to those ofthe initial characteristic learning data. That is to say, the initialcharacteristic measurement data is a characteristic data of a battery,obtained at cycles in an opening part of the cycle charging/dischargingprocess, for example charging characteristic variation data, dischargingcharacteristic variation data, thickness variation data or open circuitvoltage variation data of a battery obtained for 1 to 10 cycles.

The long term characteristic estimation data is a data calculated by theartificial neural network operation unit 30, and it is not an actuallymeasured data by the cycle charging/discharging process but an estimateddata by the artificial neural network. Attribute and kind of the longterm characteristic estimation data are substantially identical to thoseof the long term characteristic learning data. That is to say, the longterm characteristic estimation data is charging characteristic variationdata, discharging characteristic variation data, thickness variationdata or open circuit voltage variation data of a battery estimated for300 cycles, as an example.

FIG. 2 is a block diagram showing an artificial neural network having alearning ability by the artificial neural network operation unit 30according to an embodiment of the present invention.

Referring to FIG. 2, the artificial neural network having a learningability by the artificial neural network operation unit 30 includes anarrangement of neuron layers (layer1, layer2, layer3) connected inseries. In the figure, three neuron layers are arranged, but the presentinvention is not limited to the number of neuron layers. For theconvenience, the neuron layers (layer1, layer2, layer3) will be called afirst neuron layer, a second neuron layer and a third neuron layer,respectively.

The artificial neural network operation unit 30 receives initialcharacteristic measurement data P_(m) from the measurement data inputunit 20 and converts it into an initial characteristic measurementvector

{right arrow over (P)},

and then inputs it to the first neuron layer (layer1). Here, the initialcharacteristic measurement vector has a dimension of R row×1 column. Ris the number of unit data included in the initial characteristicmeasurement data. For example, assuming that the initial characteristicmeasurement data is a charging capacity variation data of a batterymeasured at regular intervals in a charging/discharging process of 1 to10 cycles and the number of charging capacity data obtained at eachcycle is 20, R is ‘20×10=100’. In this case, the initial characteristicmeasurement vector

{right arrow over (P)}

has a dimension of 100 rows×1 column, and 1 to 20 rows, 21 to 40 rows,41 to 60 rows, . . . , 181 to 200 rows respectively designate chargingcapacity variation data of 1 cycle, 2 cycle, 3 cycle, . . . , 10 cycle.

In the first neuron layer (layer 1), the initial characteristicmeasurement vector

{right arrow over (P)}

is multiplied by a weight that is an element of a weight matrix W¹, andalso added by a bias value that is an element of a bias vector

{right arrow over (b)}¹.

A result at this time, namely an intermediate result

{right arrow over (n)}¹,

is calculated into a result vector

{right arrow over (a)}¹

of the corresponding layer by a neuron transfer function f¹, and thenoutput as a second neuron layer (layer2). The following equation 1represents a numerical formula regarding the first neuron layer(layer1).{right arrow over (a)} ¹ =f ¹(W ¹ {right arrow over (P)}+{right arrowover (b)} ¹)  Equation 1

The operation method of the above first neuron layer (layer1) isidentically applied to a second neuron layer (layer2) and a third neuronlayer (layer3). However, an input vector input to each layer is anoutput vector of a last layer. The operation methods applied to thesecond neuron layer (layer2) and the third neuron layer (layer3) arerespectively as follows.{right arrow over (a)} ² =f ²(W ² {right arrow over (a)} ¹ +{right arrowover (b)} ²)  Equation 2{right arrow over (a)} ³ =f ³(W ³ {right arrow over (a)} ² +{right arrowover (b)} ³)  Equation 3

In the equations 1 to 3, the weight vectors W¹, W² and W³ respectivelyhave dimensions of S row×1 column, S row×S column and S row×S column,and the bias vector

{right arrow over (b)}¹,

{right arrow over (b)}²

and

{right arrow over (b)}³

have a dimension of S row×1 column. Here, S is the number of rows in afinal output vector

{right arrow over (a)}³

calculated by the artificial neural network. The number of rows in thefinal output vector

{right arrow over (a)}³

is identical to the number of unit data included in the long termcharacteristic estimation data.

In the present invention, the learning of the artificial neural networkmeans obtaining weight matrixes W¹, W² and W³ and bias vectors

{right arrow over (b)}¹,

{right arrow over (b)}²

and

{right arrow over (b)}³

so as to minimize or optimize a difference between the final outputvector

{right arrow over (a)}³

and the long term characteristic learning vector

{right arrow over (T)}_(t)

obtained by vectorizing the long term characteristic learning dataT_(t). For this purpose, the artificial neural network operation unit 30uses an initial characteristic learning vector

{right arrow over (P)}_(t)

and a long term characteristic learning vector

{right arrow over (T)}_(t),

obtained by vectorizing the initial characteristic learning data P_(t)and the long term characteristic learning data T_(t).

For example, assuming that the R/k number of initial characteristic dataper each cycle are obtained during k cycles for the N number ofbatteries to be learning objects and then used as initial characteristiclearning data and the S number of long term characteristic data in totalare obtained for 300 cycles and then used as long term characteristiclearning data, weight matrixes W¹, W² and W³ and bias vectors

{right arrow over (b)}¹,

{right arrow over (b)}²

and

{right arrow over (b)}³

are obtained by allowing learning of the artificial neural network using

{right arrow over (P)}_(t)

=(p₁, p₂, . . . , p_(N))[R row×N column, p₁, p₂, . . . , p_(N) arecolumn vectors] and

{right arrow over (T)}_(t)

=(t₁, t₂, . . . , t_(N))[S row×N column, t₁, t₂, . . . , t_(N) arecolumn vectors].

Here, the techniques relating to artificial neural network learning arewell known in the related art. For example, Jure Zupan, JohannGasteiger, “Neural Networks in Chemistry and Drug Design”, 2^(nd)Edition (Weinheim; New York; Chichester; Brisbane; Singapore; Toronto:Wiley-VCH, 1999) discloses a method for calculating a weight matrix Wand a bias vector

{right arrow over (b)}

by means of a correlation between input data and output data. Thus, adetailed learning algorithm of the artificial neural network using

{right arrow over (P)}_(t)

and

{right arrow over (T)}_(t)

is not explained in detail here.

The neuron transfer function f is a known transfer function in the fieldof artificial neural network. For example, Compet, Hard-limit, SymmetricHard-Limit, Log-Sigmoid, Positive Linear, Linear, Radial Basis, Satlin,Satlins, Softmax, Tan-Sigmoid, Triangular Basis, and Netinv transferfunctions may be adopted as the neuron transfer function f. However, thepresent invention is not limited thereto.

Referring to FIG. 1 again, if the final output vector

{right arrow over (a)}³

is calculated by the artificial neural network, the artificial neuralnetwork operation unit 30 outputs the final output vector (this vectoris corresponding to a long term characteristic estimation vector) as along term characteristic estimation data of the battery. Then, a display40 receives the long term characteristic estimation data and displaysthe long term characteristic estimation data in a graphic-user interfacethrough a display device. For example, if the long term characteristicestimation data is a charging capacity variation data of a batteryaccording to a charging time or a charging voltage for 300 cycles, thedisplay 40 may output the charging capacity variation data of thebattery, estimated for 300 cycles, in the form of graphic through thedisplay device. In this case, though the cycle charging/dischargingprocess is not conducted up to 300 cycles, the long term characteristicsof the battery may be easily estimated.

In another embodiment, if the final output vector

{right arrow over (a)}³

is calculated by the artificial neural network, the artificial neuralnetwork operation unit 30 may output the final output vector to a longterm characteristic evaluation unit 50 that evaluates long termcharacteristics of a battery. Then, the long term characteristicevaluation unit 50 compares the long term characteristic estimation datacalculated by the artificial neural network with a predeterminedcriterion long term characteristic data, and then, if its error is greatover a threshold value, the long term characteristic evaluation unit 50determines that the battery has bad long term characteristics. In thiscase, the long term characteristic evaluation unit 50 determines thatthe corresponding battery is inferior in aspect of long termcharacteristics, and then it may display the result in a graphic-userinterface through the display device.

For example, if the long term characteristic estimation data is relatingto a charging capacity of each charging time or charging voltage of abattery, estimated for 300 cycles, the long term characteristicevaluation unit 50 may determine that long term characteristic of thecorresponding battery is excellent only when the charging capacityestimated by the artificial neural network is greater than apredetermined criterion charging capacity of each charging time orvoltage. However, the present invention is not limited thereto indetermining excellence of long term characteristics of a battery.

Meanwhile, if the initial characteristic measurement data is out of therange of initial characteristic learning data used for learning of theartificial neural network, the reliability of the long termcharacteristic estimation data calculated by the artificial neuralnetwork is deteriorated.

In order to solve this problem, in another embodiment of the presentinvention, data structures of initial characteristic learning data andlong term characteristic learning data are defined differently such thatthe learning of the artificial neural network is differently conductedfor each data structure.

FIG. 3 shows a case that initial characteristic learning data and longterm characteristic learning data are defined in a first data structure,and FIG. 4 shows a case that initial characteristic learning data andlong term characteristic learning data are defined in a second datastructure, respectively.

Referring to the first data structure of FIG. 3, initial characteristiclearning data of the same cycle obtained for the N number of batteriesto be learning objects are arranged in a lateral direction. For example,in a first row of the initial characteristic learning data, initialcharacteristic learning data obtained in a charging/discharging processof 1 cycle for battery 1 to battery N are positioned. The other rows arearranged in the same way. Also, in a first row of the long termcharacteristic learning data, long term characteristic learning dataobtained in a charging/discharging process of 300 cycles for battery 1to battery N are positioned. Here, the range or number of cycles atwhich the initial characteristic learning data and the long termcharacteristic learning data are obtained may be changed, as apparent tothose having ordinary skill in the art. If the number of learning dataobtained for each cycle is k, the initial characteristic learning datahaving the first data structure becomes a matrix having a dimension of10 rows×(k*N) column, and the long term characteristic learning databecomes a matrix having a dimension of 1 row×(k*N) column.

Then, referring to the second data structure of FIG. 4, initialcharacteristic learning data is obtained by executing acharging/discharging process of 1 to 10 cycles for the N number ofbatteries to be learning objects, but the initial characteristiclearning data of 1 to 10 cycles obtained for each battery aresubsequently arranged in a vertical direction. Thus, in a first columnof the initial characteristic learning data, the initial characteristiclearning data obtained in a charging/discharging process of 1 to 10cycles for battery 1 are subsequently positioned. The other columns arearranged in the same way. Also, in each column of the long termcharacteristic learning data, the long term characteristic learning dataobtained in a charging/discharging process of 300 cycles for battery 1to battery N are arranged in a vertical direction. Here, the range ornumber of cycles at which the initial characteristic learning data andthe long term characteristic learning data are obtained may be changed,as apparent to those having ordinary skill in the art. If the number oflearning data obtained for each cycle is k, the initial characteristiclearning data having the second data structure becomes a matrix having adimension of (k*10) row×N column, and the long term characteristiclearning data becomes a matrix having a dimension of k row×N column.

The artificial neural network operation unit 30 converts the initialcharacteristic learning data and the long term characteristic learningdata into the first and second data structures and then allows theartificial neural network to learn individually based on the datastructure.

Here, the meaning of allowing the artificial neural network to learnindividually based on data structure is individually calculating weightmatrixes W¹, W² and W³ and bias vectors

{right arrow over (b)}¹,

{right arrow over (b)}²

and

{right arrow over (b)}³

of the artificial neural network based on the first and second datastructures.

If the artificial neural network is allowed to learn based on the firstdata structure, weight matrixes and bias vectors may be calculated suchthat long term characteristic values measured at a corresponding cycleamong 300 cycles may be estimated by column vectors (in a verticaldirection) of initial characteristic values measured at a specific cycleamong 1 to 10 cycles based on the same batteries to be learning objects.In addition, if the artificial neural network is allowed to learn basedon the second data structure, weight matrixes and bias vectors may becalculated such that long term characteristic values for the entire 300cycles may be estimated by column vectors (in a vertical direction) ofinitial characteristic values for the entire 1 to 10 cycles based on thesame batteries to be learning object.

After the individual learning of the artificial neural network based onthe data structure is completed, if initial characteristic measurementdata of a battery whose long term characteristics should be determinedis input, the artificial neural network operation unit 30 converts thedata structure of the initial characteristic measurement data into thefirst data structure and the second data structure and then applies thelearned artificial neural network based on each data structure tocalculate two long term characteristic estimation data.

At this time, when calculating long term characteristic estimation datafrom the initial characteristic measurement data having the first datastructure, the artificial neural network operation unit 30 estimateslong term characteristic values for 300 cycles corresponding to thelocation of a column vector by using the column vector composed ofinitial characteristic values of each measurement time for 1 to 10cycles. In this method, initial characteristic values are associated at10 cycles different from each other to estimate long term characteristicdata for 300 cycles one by one. Meanwhile, in case long termcharacteristic estimation data is calculated from the initialcharacteristic measurement data having the second data structure, acolumn vector composed of initial characteristic values for the entire 1to 10 cycles is used to estimate long term characteristic values for theentire 300 cycles. In this method, long term characteristic data for 300cycles are estimated once with reference to initial characteristicvalues of the entire 10 cycles.

If the artificial neural network uses different approaches to estimatelong term characteristic data for 300 cycles, though artificial neuralnetworks having learned based on different data structures are applied,there is substantially no error between two long term characteristicestimation data if the initial characteristic measurement data does notdepart from the range of the initial characteristic learning data. It isbecause the artificial neural network has well learned to estimate thesubstantially identical long term characteristic learning dataregardless of data structure of the initial characteristic measurementdata within the range of initial characteristic learning data used forlearning. In other words, if the initial characteristic measurement datadeparts from the range of the initial characteristic learning data, anerror between two long term characteristic estimation data is increasedif artificial neural networks having learned based on different datastructures are applied. Thus, by using this phenomenon, it is possibleto easily evaluate reliability of long term characteristic estimationdata.

That is to say, the artificial neural network operation unit 30 obtainstwo long term characteristic estimation data from initial characteristicmeasurement data having different data structures, and then outputs themto the long term characteristic evaluation unit 50. Then, the long termcharacteristic evaluation unit 50 calculates an error between two longterm characteristic estimation data, and then, if the error exceeds athreshold value, the long term characteristic evaluation unit 50determines that the initial characteristic measurement data used forestimating long term characteristics of a battery is out of thequalitative and quantitative range of the initial characteristiclearning data used for learning of the artificial neural network. Inthis case, the long term characteristic evaluation unit 50 may display amessage informing of low reliability of the long term characteristicestimation data in a graphic-user interface through the display device.

On the contrary, if an error between two long term characteristicestimation data is lower than a threshold value, the long termcharacteristic evaluation unit 50 determines that the initialcharacteristic measurement data used for estimating long termcharacteristics of a battery is within the qualitative and quantitativerange of the initial characteristic learning data used for learning ofthe artificial neural network. In this case, the long termcharacteristic evaluation unit 50 finally determines vector any one ofthe two long term characteristic estimation data or vector average dataof the two long term characteristic estimation data as a long termcharacteristic estimation data and then displays a varying pattern ofthe long term characteristic estimation data in a graphic-user interfacethrough the display device. Further, the long term characteristicevaluation unit 50 may compare the finally determined long termcharacteristic estimation data with a criterion long term characteristicdata to determine whether the long term characteristics of the batteryare excellent, and then display the result in a graphic-user interfacethrough the display device.

The system for estimating long term characteristics of a battery, asexplained above, samples a plurality of batteries whose long termcharacteristics will be estimated for each battery lot after batteryproduction is completed, then conducts a cycle charging/dischargingprocess for each sampled battery to obtain initial characteristicmeasurement data, and then tests long term characteristics of eachsampled battery by using the obtained initial characteristic measurementdata, so it may be useful for sampling checking of long termcharacteristic quality of a battery lot.

FIG. 5 is a block diagram showing a system for estimating long termcharacteristics of a battery according to a second embodiment of thepresent invention.

The system for estimating long term characteristics of a batteryaccording to the second embodiment is for estimating long termcharacteristics of a battery by using a charging characteristic of abattery, measured in a battery activation process.

This system uses a charging voltage variation data, a charging currentvariation data or a charging capacity variation data of a battery whenthe battery is initially charged in the battery activation process, asan initial characteristic measurement data of the battery.

Thus, the system according to the second embodiment further includes aninitial characteristic measurement sensor 60, differently from the firstembodiment. The initial characteristic measurement sensor 60 detects acharging voltage of both terminals of a battery, a charging currentintroduced into the battery, or a charging capacity of the battery atregular intervals when the battery put into an activation process isinitially charged, and then outputs it to the measurement data inputunit 20. Then, the measurement data input unit 20 inputs the initialcharacteristic measurement data output from the initial characteristicmeasurement sensor 60 into the artificial neural network operation unit30.

The artificial neural network operation unit 30 receives chargingvoltage variation data, charging current variation data or chargingcapacity variation data of a battery, measured in an activation processof a battery designated as a learning object, as initial characteristiclearning data and also receives charging characteristic variation data,discharging characteristic variation data, thickness variation data oropen circuit voltage variation data of a battery, measured for apredetermined cycles, for example 300 cycles, after putting a batterydesignated as a learning object, as long term characteristic learningdata through the learning data input unit 10, and then allows theartificial neural network to learn. Also, the artificial neural networkoperation unit 30 calculates and outputs long term characteristicestimation data by applying the learned artificial neural networkwhenever an initial characteristic measurement data measured at thebattery activation process is input from the measurement data input unit20.

The system of the second embodiment has the may obtain the initialcharacteristic measurement sensor 60, so it may obtain initialcharacteristic measurement data from the activation process in realtime. Thus, the system of the second embodiment may be applied to thetotal inspection for long term characteristics of a battery in thebattery activation process. Also, the kind of initial characteristicmeasurement data of the second embodiment, used for calculating initialcharacteristic learning data and long term characteristic estimationdata used for learning of the artificial neural network, is differentfrom that of the first embodiment. Except for the above, the system ofthe second embodiment is substantially identical to that of the formerembodiment.

Now, a method for estimating long term characteristic of a batteryaccording to an embodiment of the present invention is explained.

FIG. 6 is a schematic flowchart illustrating operation sequence of thesystem for estimating long term characteristics of a battery accordingto the first embodiment of the present invention.

Referring to FIGS. 1 and 6, first, the artificial neural networkoperation unit 30 collects initial characteristic learning data and longterm characteristic learning data of a battery to be a learning objectthrough the learning data input unit 10 (S100). Here, the initialcharacteristic learning data and the long term characteristic learningdata are already explained above.

Subsequently, the artificial neural network operation unit 30 allows theartificial neural network to learn using the collected initialcharacteristic learning data and the collected long term characteristiclearning data (S110).

After the learning of the artificial neural network is completed, theartificial neural network operation unit 30 receives initialcharacteristic measurement data of a battery to be an object forestimation of long term characteristics through the measurement datainput unit 20 (S120). The initial characteristic measurement data may beobtained in the way of sampling a predetermined number of batteries froma battery lot completely produced, and then executing a cyclecharging/discharging process for the sampled batteries. As analternative, the initial characteristic measurement data may be obtainedusing the initial characteristic measurement sensor 60 when a batteryput into the activation process is initially charged (see FIG. 5).

After that, the artificial neural network operation unit 30 calculateslong term characteristic estimation data of predetermined long termcycles by applying the learned artificial neural network to the inputinitial characteristic measurement data.

And then, the artificial neural network operation unit 30 displays thecalculated long term characteristic estimation data on the display 40.Then, the display 40 displays the long term characteristic estimationdata in a graphic-user interface through a display device (S140).

As an alternative, the artificial neural network operation unit 30outputs the calculated long term characteristic estimation data to thelong term characteristic evaluation unit 50. Then, the long termcharacteristic evaluation unit 50 evaluates long term characteristicquality of the battery by comparing the calculated long termcharacteristic estimation data with a criterion long term characteristicdata, and then displays the result in a graphic-user interface throughthe display device (S150).

FIG. 7 is a flowchart illustrating operation sequence of the system forestimating long term characteristics of a battery according to thesecond embodiment of the present invention.

Referring to FIGS. 1 and 7, first, the artificial neural networkoperation unit 30 collects initial characteristic learning data and longterm characteristic learning data of a battery to be a learning objectthrough the learning data input unit 10 (S200). Here, the initialcharacteristic learning data and the long term characteristic learningdata are already explained above.

Subsequently, the artificial neural network operation unit 30 convertsthe collected initial characteristic learning data and the collectedlong term characteristic learning data into first and second datastructures (S210). After than, the artificial neural network is allowedto learn based on each data structure (S220). Here, the first and seconddata structures are already explained above in detail with reference toFIGS. 3 and 4.

After the learning of the artificial neural network based on each datastructure is completed, the artificial neural network operation unit 30receives initial characteristic measurement data of a battery to be anobject for estimation of long term characteristics through themeasurement data input unit 20 (S230). The initial characteristicmeasurement data may be obtained in the way of sampling a predeterminednumber of batteries from a battery lot completely produced, and thenexecuting a cycle charging/discharging process for the sampledbatteries. As an alternative, the initial characteristic measurementdata may be obtained using the initial characteristic measurement sensor60 when a battery put into the activation process is initially charged(see FIG. 5).

After that, the artificial neural network operation unit 30 converts theinput initial characteristic measurement data into first and second datastructures (S240). And then, the learned artificial neural network basedon each data structure is applied to the input initial characteristicmeasurement data according to the first and second data structures,thereby calculating two long term characteristic estimation data forpredetermined long term cycles (S250).

And then, the artificial neural network operation unit 30 displays thecalculated two long term characteristic estimation data on the display40. Then, the display 40 displays the two long term characteristicestimation data in a graphic-user interface through a display device(S260).

As an alternative, the artificial neural network operation unit 30outputs the two calculated long term characteristic estimation data tothe long term characteristic evaluation unit 50. Then, the long termcharacteristic evaluation unit 50 calculates an error between the twolong term characteristic estimation data by comparing them with eachother, determines the reliability of the long term characteristicestimation data according to whether the error exceeds a thresholdvalue, and then displays the result in a graphic-user interface throughthe display device (S270).

Further, the long term characteristic evaluation unit 50 decides any oneof the two long term characteristic estimation data or their vectoraverage data as long term characteristic estimation data, evaluates longterm characteristic quality of the battery by comparing the decided longterm characteristic estimation data with a criterion long termcharacteristic data, and then displays the result in a graphic-userinterface through the display device (S280).

The system and method for estimating long term characteristics of abattery according to the present invention may be implemented in aprogram instruction form capable of being executed by various computermeans, and then recorded in a computer-readable medium. Thecomputer-readable medium may include program instructions, data filesand data structures, in single or in combination. The programinstruction recorded in the medium may be specially designed andconfigured for the present invention or any other usable one well knownin the computer program field. The computer-readable recording mediumincludes, for example, magnetic media such as a hard disk, a floppy diskand a magnetic tape; optical media such as CD-ROM and DVD;magneto-optical media such as a floptical disk; and hardware devicesspecifically configured to store and execute program instructions suchas ROM, RAM and flash memory. The medium may also be a transmissionmedium such as a waveguide and an optical or metal wire having acarrier, which transmits a signal designating program instruction ordata structure. The program instruction includes, for example, a machinecode made by a complier or a high-level programming language codeexecutable by a computer using an interpreter or the like. The hardwaredevice may be configured as being operated as at least one softwaremodules for executing the operations of the present invention, or viceversa.

FIG. 8 is a block diagram showing a general-purpose computer systemadoptable for executing the operation method of the system forestimating long term characteristics of a battery according to thepresent invention.

Referring to FIG. 8, the general-purpose computer system 400 includes atleast one processor 410 connected to a main storage device having RAM420 and ROM 430. The processor 410 is also called CPU. As well known inthe art, ROM 430 plays a role of unilaterally transmitting data andinstructions to the processor 410. RAM 420 is commonly used forbi-directionally transmitting data and instructions to the processor410. RAM 420 and ROM 430 may have any suitable shape of acomputer-readable medium. A mass storage device 440 is bi-directionallyconnected to the processor 410 to give an additional data storagecapability thereto, and it may be any one of the above computer-readablemedia. The mass storage device 440 is used for storing programs, dataand so on, and it is commonly an auxiliary storage device such as a harddisk, whose speed is lower than a main storage device. A specific massstorage device such as CD-ROM 460 may also be used. The processor 410 isconnected to at least one I/O interface 450 such as a video monitor, atrackball mouse, a keyboard, a microphone, a touch screen-type display,a card reader, a magnetic or paper tape reader, a voice or handwritingrecognizer, a joystick, or other well-known computer I/O devices.Finally, the processor 410 may be connected to a wired or wirelesscommunication network through a network interface 470. The above methodmay be executed through the network connection. The above devices andtools are well known to those having ordinary skill in the computerhardware and software fields. Meanwhile, the hardware device may also beconfigured as being operated as at least one software module so as toexecute operations of the present invention.

The present invention has been described in detail. However, it shouldbe understood that the detailed description and specific examples, whileindicating preferred embodiments of the invention, are given by way ofillustration only, since various changes and modifications within thespirit and scope of the invention will become apparent to those skilledin the art from this detailed description.

INDUSTRIAL APPLICABILITY

According to the present invention, reliable long term characteristicsof a battery may be rapidly evaluated using initial characteristics ofthe battery, so various problems of the conventional post-shipment longterm characteristic evaluation method can be solved.

That is to say, it is possible to reduce a cost required for retrievinga battery lot determined as having bad quality. Also, since inferiorityfactors of long term characteristics may be rapidly recognized andremoved, it is possible to prevent addition production of batteries withinferior long term characteristics. In addition, it is possible tosupply only batteries with excellent long term characteristics toconsumers, and also it is possible to lessen a load on the equipmentused for a cycle charging/discharging process of a battery.

In another aspect of the present invention, since characteristicsmeasured for all batteries when the batteries are manufactured, forexample a charging data in an activation process, are used, it ispossible to realize the entire inspection of the battery.

The invention claimed is:
 1. A system for estimating long termcharacteristics of a battery, comprising: a learning data input unit forreceiving initial characteristic learning data, obtained at a cycleconducted in an opening part of a repeated cycle charging/dischargingprocess with respect to a battery to be a learning object and long termcharacteristic learning data, obtained at a cycle conducted in a latterpart of the cycle charging/discharging process; a measurement data inputunit for receiving initial characteristic measurement data, obtained ata cycle conducted in an opening part of the cycle charging/dischargingprocess with respect to a battery to be an object for estimation of longterm characteristics; an artificial neural network operation unit forconverting the initial characteristic learning data and the long termcharacteristic learning data into first and second data structures,allowing an artificial neural network to learn the initialcharacteristic learning data and the long term characteristic learningdata based on each data structure, converting the input initialcharacteristic measurement data into first and second data structures,and individually applying the learned artificial neural networkcorresponding to each data structure to calculate and output long termcharacteristic estimation data based on each data structure; and a longterm characteristic evaluation unit for calculating an error of theoutput long term characteristic estimation data of each data structureand determining reliability of the long term characteristic estimationdata depending on the error.
 2. The system according to claim 1, whereinthe learned artificial neural network based on each data structure hasat least one neuron layer arranged in series, and wherein the neuronlayer converts an input vector into an output vector such that a biasvector and a weight matrix calculated by the learning of the artificialneural network are reflected on the input vector, the input vector onwhich the bias vector and the weight matrix are reflected is processedby a neuron transfer function, and then result of the neuron transferfunction is output as an output vector.
 3. The system according to claim2, wherein, in the serial arrangement of the neuron layer, the firstneuron layer has an input vector composed of initial characteristicmeasurement data.
 4. The system according to claim 1, wherein the datarelating to initial characteristics includes a charging characteristicvariation data of a battery, measured in a battery activating process;or a charging characteristic variation data, a dischargingcharacteristic variation data, a thickness variation data, or an opencircuit voltage variation data of a battery, obtained by measurement ofinitial cycle characteristics, and wherein the data relating to longterm characteristics includes a charging characteristic variation data,a discharging characteristic variation data, a thickness variation data,or an open circuit voltage variation data of a battery at predeterminedlong term cycles.
 5. The system according to claim 1, further comprisingan initial characteristic measurement sensor for measuring a chargingcharacteristic of a battery put into an activating process and thenoutputting the measured charging characteristic as an initialcharacteristic measurement data, wherein the measurement data input unitreceives the initial characteristic measurement data from the initialcharacteristic measurement sensor.
 6. The system according to claim 1,further comprising a display for receiving the long term characteristicestimation data calculated based on each data structure from theartificial neural network operation unit to display the long termcharacteristic estimation data in a graphic-user interface through adisplay device.
 7. The system according to claim 1, wherein, in case theerror is less than a criterion value, the long term characteristicevaluation unit determines any one of the long term characteristicestimation data calculated based on each data structure or average dataof the long term characteristic estimation data as a long termcharacteristic estimation data, and then outputs the long termcharacteristic estimation data.
 8. The system according to claim 7,wherein the long term characteristic evaluation unit determines longterm characteristic quality of the battery by comparing the determinedlong term characteristic estimation data with a criterion long termcharacteristic data.
 9. The system according to claim 8, wherein thelong term characteristic evaluation unit outputs a long termcharacteristic quality determination result of the battery in agraphic-user interface through a display device.
 10. A method forestimating long term characteristics of a battery, comprising: (a)receiving initial characteristic learning data and long termcharacteristic learning data, respectively obtained at a cycle conductedin an opening part and at a cycle conducted in an opening part of arepeated cycle charging/discharging process with respect to a battery tobe a learning object; (b) converting the received initial characteristiclearning data and the received long term characteristic learning datainto first and second data structures and then individually allowing anartificial neural network to learn based on each data structure; (c)receiving initial characteristic measurement data, obtained at a cycleconducted in an opening part of the cycle charging/discharging processwith respect to a battery to be an object for estimation of long termcharacteristics; (d) converting the received initial characteristicmeasurement data into first and second data structures, then applyingthe learned artificial neural network corresponding to each datastructure thereto, and then calculating and outputting long termcharacteristic estimation data based on each data structure; and (e)calculating an error of the output long term characteristic estimationdata based on each data structure and then determining reliability ofthe long term characteristic estimation data depending on the error. 11.The method according to claim 10, wherein the learned artificial neuralnetwork based on each data structure has at least one neuron layerarranged in series, and wherein, in the step (d), the process ofapplying the learned artificial neural network based on each datastructure includes: (d1) converting the initial characteristicmeasurement data into an input vector; (d2) inputting the convertedinput vector into a first neuron layer of the neuron layer arrangement;(d3) each neuron layer of the neuron layer arrangement reflecting a biasvector and a weight matrix calculated by the learning of the artificialneural network on the input vector and then processing the input vectorby a neuron transfer function such that the input vector is convertedinto an output vector and then output; and (d4) a last neuron layer ofthe neuron layer arrangement outputting a long term characteristicestimation vector as an output vector.
 12. The method according to claim10, wherein the data relating to initial characteristics includes acharging characteristic variation data of a battery, measured in abattery activating process; or a charging characteristic variation data,a discharging characteristic variation data, a thickness variation data,or an open circuit voltage variation data of a battery, obtained bymeasurement of initial cycle characteristics, and wherein the datarelating to long term characteristics includes a charging characteristicvariation data, a discharging characteristic variation data, a thicknessvariation data, or an open circuit voltage variation data of a batteryat predetermined long term cycles.
 13. The method according to claim 10,wherein the initial characteristic learning data is a chargingcharacteristic variation data of the battery to be a learning object,put into a battery activating process, and the long term characteristiclearning data is a charging characteristic variation data, a dischargingcharacteristic variation data, a thickness variation data, or an opencircuit voltage variation data of the battery to be a learning object atpredetermined long term cycles, and wherein the step (c) includes:putting the battery to be an object for long term characteristicestimation into the battery activating process; measuring a chargingcharacteristic variation from the battery; and receiving the measuredcharging characteristic variation data as an initial characteristicmeasurement data.
 14. The method according to claim 10, furthercomprising: visually displaying the long term characteristic estimationdata.
 15. The method according to claim 10, further comprising:determining any one of the long term characteristic estimation datacalculated based on each data structure or average data of the long termcharacteristic estimation data as a long term characteristic estimationdata; comparing the determined long term characteristic estimation datawith a criterion long term characteristic data to determine long termcharacteristic quality of the battery.
 16. The method according to claim15, further comprising: visually displaying a long term characteristicquality determination result of the battery.